Impacts of COVID-19 on trans and non-binary people in Canada: a qualitative analysis of responses to a national survey
Bibliographic record
Abstract
BACKGROUND: Emerging international evidence indicates the COVID-19 pandemic has exacerbated socioeconomic and health challenges faced by transgender (trans) and non-binary populations globally. This qualitative study is among the first to characterize impacts of the pandemic on these groups in Canada. METHODS: Drawing on data from the Trans PULSE Canada survey (N = 820), we used thematic analysis to examine the free-form responses of 697 participants to one open-ended question on impacts of the pandemic. We first organized responses into descriptive themes, and then used this preliminary analytical process to construct more refined, higher order themes that provided a rich account of the pandemic's impacts. RESULTS: Our results are organized into five themes that highlight the pandemic's impacts on trans and non-binary populations in Canada. These include: (1) reduced access to both gender-affirming and other healthcare, (2) heightened financial, employment, and housing precarity, (3) strained social networks in an era of physical distancing and virtual communication, (4) an intensification of safety concerns, and (5) changes in experiences of gender affirmation. CONCLUSION: Our findings highlight the pandemic's systemic impacts on the lives of trans and non-binary people in domains such as healthcare, employment, and housing, and on the social networks of these groups, many of which reflect an exacerbation of pre-existing inequities. Based on our analysis, we recommend that public health researchers, policymakers, and practitioners attend to the structural impacts of the pandemic on these groups as primary sites of inquiry and intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".